Metabolic Profiling of Different Parts of Acer truncatum from the

Article Cite This: J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Metabolic Profiling of Different Parts of Acer truncatum from the Mongolian Plateau Using UPLC-QTOF-MS with Comparative Bioactivity Assays Ronghui Gu,† Levi Rybalov,‡,⊥ Adam Negrin,§,# Taylan Morcol,§,# Weiwen Long,∥ Amanda K. Myers,∥ Giorgis Isaac,∇ Jimmy Yuk,∇ Edward J. Kennelly,*,§,# and Chunlin Long*,†,○

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†

College of Life and Environmental Sciences, Minzu University of China, 27 Zhong-Guan-Cun South Avenue, Haidian, Beijing 100081, People’s Republic of China ‡ Department of Chemistry and §Department of Biological Sciences, Lehman College, City University of New York, 250 Bedford Park Boulevard West, Bronx, New York 10468, United States ⊥ Macaulay Honors College, City University of New York, 35 West 67th Street, New York City, New York 10023, United States # Ph.D. Program in Biology, The Graduate Center, City University of New York, 365 Fifth Avenue, New York, New York 10016 United States ∥ Department of Biochemistry and Molecular Biology, Wright State University, 3640 Colonel Glenn Highway, Dayton, Ohio 45435, United States ∇ Waters Corporation, 34 Maple Street, Milford, Massachusetts 01757, United States ○ Key Laboratory of Ethnomedicine, Ministry of Education, Minzu University of China, Beijing 100081, People’s Republic of China S Supporting Information *

ABSTRACT: Acer truncatum is an important ornamental, edible, and medicinal plant resource in China. Previous phytochemical research has focused on the leaf (AL) due to its long history as a tea for health. Other parts such as the branch (ABr), bark (ABa), fruit (AF), and root (AR) have drawn little attention regarding their metabolites and bioactivities. The strategy of an in-house chemical library combined with Progenesis QI informatics platform was applied to characterize the metabolites. A total of 98 compounds were characterized or tentatively identified, including 63 compounds reported from this species for the first time. Principal component analysis showed the close clustering of ABr, ABa, and AR, indicating that they share similar chemical components, while AL and AF clustered more distantly. By multiple orthogonal partial least-squares discriminant analyses (OPLS-DA), 52 compounds were identified as potential marker compounds differentiating these different plant parts. The variable influence on projection score from OPLS-DA revealed that catechin, procyanidins B2 or B3, and procyanidins C1 or C2 are the significant metabolites in ABa extracts, which likely contribute to its antioxidant and cytotoxic activities. KEYWORDS: Acer truncatum, metabolites, antioxidant, cytotoxicity, metabolomics, LC-MS, chemometrics

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INTRODUCTION

cirrhosis, and cerebrovascular diseases in Chinese folk medicine.4,5 In recent years, phytochemical and pharmacological investigations of A. truncatum have been undertaken. The AF wing was reported to contain quercetin, kaempferol, and isorhamnetin, and was demonstrated to have sedative and anticoagulant bioactivities.6 The oil of AF, which is rich in nervonic acid (∼6%), phytosterols, tocopherols, and βcarotene, exhibited inhibition of tumor growth activity.7,8 The extracts of AL showed a wide-range of bioactivities, including antitumor, antioxidant, inhibition of fatty acid synthase (FAS), and weight reducing properties, due to

Acer truncatum Bunge (Sapindaceae) is a perennial deciduous maple tree commonly known as Shantung maple, Chinese purpleblow maple, or yuan bao feng in Chinese. Acer species are important worldwide as food, medicine, and ornamentals, such as Acer saccharum (sugar maple), Acer rubrum (red maple), and Acer nigrum (black maple). Acer truncatum is native to China, Japan, and Korea, but it has been cultivated widely throughout Europe, North America, and Asia.1,2 It is one of the most important native tree and plant resources in China due to its ornamental, edible, and medicinal values. Our ethnobotanical survey found that local people in Inner Mongolia prepare roots (AR) and branches (ABr) as a traditional tea,3 cook and eat seeds found inside its two-winged nutlet indehiscent fruits (AF), as well as use the AR to treat joint pains, fractures, and wounds as recorded in Mongolian traditional medicine. The leaves (AL) are made into a well-known traditional Chinese tea, which is used in treating angina pectoris, coronary artery © XXXX American Chemical Society

Received: August 3, 2018 Revised: January 7, 2019 Accepted: January 7, 2019

A

DOI: 10.1021/acs.jafc.8b04035 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry

MO, USA). For UPLC-MS analysis, LC/MS-grade acetonitrile, methanol, and water were purchased from J.T. Baker (Philipsburg, NJ, USA), and formic acid was purchased from Sigma-Aldrich (St. Louis, MO, USA). The bioactivity assay chemicals, 2,2′-diphenyl-1picrylhydrazyl (DPPH), gallic acid (GA), and dimethyl sulfoxide (DMSO), were purchased from Sigma-Aldrich. CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) kit was from Promega Corporation (Madison, WI, USA). For sample extraction, analytical grade methanol was from VWR Inc. (Bridgeport, PA, USA), and ultrapure water was prepared by Milli-RO 12 plus system (Millipore, MA, USA). Sample Preparation. Air-dried AL, ABr, ABa, AF, and AR were pulverized and passed through a 40-mesh sieve. The extraction procedure was modified from a published method.24 Briefly, each powdered sample (0.2 g) was stirred with 10 mL of methanol−water (80:20 v/v) followed by sonication for 20 min and then centrifugation (4000 rpm) for 5 min using a Thermo scientific IEC Centra GP8 centrifuge (Waltham, MA, USA). The supernatant was transferred to a 20 mL vial, and the extraction process was repeated on the same sample for a second time. The combined supernatant (6 mL) was passed through a 0.45 μm polytetrafluoroethylene (PTFE) syringe filter (VWR Bridgeport, PA, USA) and evaporated under nitrogen flow at room temperature. To validate the analytical method and monitor the stability of the system, a pooled quality control (QC) sample was prepared by mixing the same volume (0.2 mL each) from the 30 samples of A. truncatum. All 31 dried extracts were kept in a −20 °C freezer. Prior to UPLC-QTOF-MS analysis, each dried extract sample was dissolved in 80% LC/MS grade methanol to a concentration of 2 mg/mL and then filtered (PTFE, 0.45 μm) into an autosample vial. Chromatographic Conditions. The UPLC separation was performed on a Waters ACQUITY UPLC System (Waters, Milford, MA, USA) with an ACQUITY UPLC BEH C18 column (2.1 mm × 50 mm, i.d. 1.7 μm) and an ACQUITY UPLC BEH C18 1.7 μm VanGuard Precolumn (2.1 mm × 5 mm; Waters, Milford, MA, USA), which was controlled by Masslynx 4.1 software. The flow rate was 0.5 mL/min, and the solvent system consisted of (A) 0.1% aqueous formic acid and (B) acetonitrile with 0.1% formic acid. A linear gradient elution was applied as follows: 0−1.0 min, 3−13% B; 1.0−3.5 min, 13−40% B; 3.5−7.0 min, 40−70% B; 7.0−8.0 min, 70−97% B; 8.0−9.2 min, 97% B; 9.2−9.7 min, 97−3% B, 9.7−11 min, 3% B. The column temperature was held at 40 °C, and 1 μL of each sample solution was injected. Three injections were performed for each sample including blanks, and were randomized before data acquisition. Mass Spectrometry Conditions. A Waters Xevo G2 QTOF (Waters, Milford, MA, USA) equipped with Z-Spray ESI source and controlled by MassLynx 4.1 was used for MS data acquisition. Both positive and negative ionization modes were applied to acquire data under a mass range 100−1500 Da with a scan time of 1 s. The capillary voltages were 3.0 kV for positive mode and 2.5 kV for negative mode, and the sampling cone voltage was 30 V. The desolvation and source temperatures were 400 and 110 °C, respectively. The desolvation gas flow rate was 800 L/h, and the cone gas flow rate was 50 L/h. MSe data were obtained in centroid mode with a mass range 50−1500 Da in both low-energy (function 1) and high-energy (function 2) scan functions. For the low-energy scan function, the collision energy was 6 V, and the scan time was 1 s. For the high-energy scan function, a collision energy ramp of 20−60 V was used with a scan time of 1 s. Leucine-enkephalin (1 μg/mL) was used as lock-mass solution. This solution was introduced by LockSpray at 20 μL/min and used to generate the reference ions at m/z 556.2771 (positive mode) and m/z 554.2615 (negative mode). In-House Library of the Genus Acer Construction. A customized in-house library of compounds from the genus Acer using Progenesis SDF Studio was constructed. A three-step strategy was applied to construct the in-house library. First, the compounds previously reported from Acer species were identified using the search terms “Acer and maple” from electronic sources such as Google Scholar, SciFinder, Web of Science, and CNKI. Second, the selected

substantial content of tannins, chlorogenic acid, and flavonoids.2,5,9−11 Three main compounds, quercetin-3-O-L-rhamnoside,4 methyl gallate,12 and 1,2,3,4,6-penta-O-galloyl-β-D-glucose (PGG),13 were isolated from AL and are thought to be the key contributors to the bioactivity of AL. However, other parts of A. truncatum such as ABr, AR, and bark (ABa) have a few reports of phytochemistry and bioactivity. Clearly, the different parts of A. truncatum have shown various medicinal values, but further development and utilization of this species remain challenging due to the limited evaluation of its metabolites and bioactivity. The continuous development of liquid chromatography tandem mass spectrometry (LC-MS) has provided an effective method for chemical identification of global metabolites in plant extracts. For example, ultraperformance LC (UPLC) and high-resolution MS (HRMS) facilitate the detection of hundreds of compounds in an untargeted experiment even in low quantities.14,15 In addition the utilization of dataindependent acquisition (DIA) modes such as MS data acquisition with alternating low and elevated energy (MSe) allows higher confidence in compound identification as it enables researchers to acquire low- and high-energy spectra of every peak without a selection criteria compared to traditional data-dependent MS acquisitions which require additional injections for MS/MS information.16−18 This specific metabolomics approach has been applied widely to compare the metabolite profiles of plants from different geographical origins,19,20 plants grown in different seasons,21 and different plant parts.22,23 Until now, the use of UPLC coupled to quadrupole/time-of-flight mass spectrometry (UPLC-QTOFMS/MS) to analyze A. truncatum samples has been used to characterize phenolic components of AL extracts.2 However, the systematic characterization of the differential metabolites in five plant parts of A. truncatum has not been reported. In the current study, a UPLC-QTOF-MS method is coupled with data analysis using bioinformatics tools to compare the characteristics of the metabolites of AL, AF, ABr, ABa, and AR of A. truncatum and identify specific marker compounds of these parts. In addition, previous research on AL has examined its biological activity, especially with regard to antioxidant2,10,12 and cytotoxic activity on the cancer cell lines MCF-7, CAES17, BEL-7402, and BGC-823.9 However, there have not been studies on the antioxidant and cytotoxicity of other parts of A. truncatum. Therefore, a comparative antioxidant and cytotoxic screens of these five plant parts was conducted in vitro as well, allowing for the correlation between metabolites and bioactivity of the plant parts of A. truncatum, and potentially providing alternative sources of antioxidant or anticancer compounds.

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MATERIALS AND METHODS

Plant Materials. The five different parts (AL, ABr, ABa, AF, and AR) of A. truncatum were collected from trees in Tongliao, Inner Mongolia, China, in August 2016. The identification of voucher specimens was authenticated by Professor Chunlin Long from Minzu University of China and deposited at the Ethnobotanical Laboratory in Minzu University of China in Beijing. Chemicals. Reference standards were applied to compare the retention time, MS data with identified compounds, including (−)-epicatechin-3-O-gallate (ECG), (−)-epigallocatechin (EGC), (−)-epigallocatechin-3-O-gallate (EGCG), (+)-catechin, gallic acid, (−)-gallocatechin-3-O-gallate (GCG), myricetin, quercetin, quinic acid, and rutin, were each purchased from Sigma-Aldrich (St. Louis, B


Article


Figure 1. Progenesis QI showing an identification of GCG from the filtered metabolic features (A) common adduct ions for GCG. (B) Identification of GCG. The experimental spectra which are matched against the in silico fragment ions are highlighted in red. The circle on the top of the red spectra shows the structure, measured m/z, theoretical m/z, and corresponding mass error in ppm. (C) Displays low energy major precursor exact mass GCG adduct ions and corresponding high energy fragment ions. compounds and transfer individual structure files (.mol) were verified by searching its name or structure (found in original articles) in SciFinder to obtain CAS numbers. Finally all .mol files were integrated into an in-house library using Progenesis SDF Studio. Mass Data Processing and Analysis. The centroid MSe raw data in negative mode were processed by Progenesis QI 2.3 software (Waters, Milford, MA, USA). The general procedure in this software included data import, chromatographic peak alignment, experimental design setup, peak picking, normalization, deconvolution, compound identification, and compound statistics. The imported 110 runs were aligned based on an automatically selected QC sample. The peak picking conditions were set as follows: all runs, limits (automatic), sensitivity (default 3), chromatographic peak width (minimum peak width), and retention time (0.06 to 8.0 min). The selected adduct ion forms, including [M − H2O−H]−, [M − H]−, [M + FA−H]−, [M + Na−2H]−, [M + K−2H]−, [2M + FA−H]−, and [2M − H]−, were applied to deconvolute the spectral data. The processed data were then exported to EZinfo 3.0 (Umetrics, Umeå, Sweden) for multivariate statistical analysis including principal component analysis (PCA) with Pareto scaling mode, and orthogonal partial least-squares discriminant analysis (OPLS-DA). An S-plot was employed to identify the potential marker compounds that are responsible for the differentiation of groups. The significant differential retention timeexact mass (RT-EM) pairs in the S-plots were selected and imported back into Progenesis QI for compound identification. After filtering by means of ANOVA p value ≤0.05 and max fold change ≥2, the filtered RT-EM pairs were identified based on three individual search methods in Progenesis QI: method (1) Progenesis MetaScope with an in-house library and reference standard (.csv) files (parameters: precursor tolerance 10 ppm, retention time within 0.1 min, and theoretical fragment tolerance 10 ppm); method (2) A commercial Metabolic Profiling CCS Library package (Waters, Milford, MA, USA) (parameters: both precursor tolerance and fragment tolerance 10 ppm); method (3) Elemental composition (parameters: elements and corresponding number were set as H (0−50), C (0−50), N (0−

5), and O (0−30), set precursor tolerance 10 ppm, isotope similarity 95%). DPPH Scavenging Activity. The DPPH free radical scavenging was applied to test the antioxidant properties of extracts from different parts of A. truncatum. DPPH assay was performed using a previously described method.25 In brief, 50 μL of test samples serially diluted in DMSO were combined with 150 μL of DPPH in 95% ethanol/water (400 μM) in a 96-well microplate. After 30 min incubation at 37 °C in the dark, the absorbance was measured at 517 nm by a Synergy H1 Hybrid reader (BioTek, VT, USA). The percentage of inhibition was calculated using the following equation: % inhibition = (absorbancecontrol − absorbancesample)/(absorbancecontrol − absorbanceblank) × 100. GA was used as a reference standard for its high antioxidant activity, and this experiment was conducted in triplicate for each sample. IC50 values were calculated by linear regression analysis. P < 0.05 was considered as significant difference, which calculated by oneway ANOVA analysis and Tukey’s multiple comparisons test in GraphPad software (GraphPad Software, Inc., La Jolla, CA, USA). Cell Viability Assay. Dried extracts of different parts of A. truncatum were dissolved in 80% ethanol. The cell viability assay was performed with the CellTiter 96 AQueous One Solution Cell Proliferation Assay (MTS) kit. HepG2 cells were seeded at 5000 cells per well on 96-well plates. After a 24 h incubation, the used media was replaced with fresh media containing either vehicle (1% ethanol as the final concentration) or different concentrations of samples (0, 25, 50, 75, 100, and 150 μg/mL). All vehicles and treatments were repeated in triplicate. Cell growth was determined by MTS assay after 72 h of culture following the manufacturer’s instructions (Promega, Madison, WI, USA). Simply, each 20 μL of MTS mixed with 100 μL of prewarmed media was added to replace the old media in each well. After incubation for 75 min, the absorbance was read at 490 nm. Raw values were subtracted from a blank (the wells without cells but with media containing MTS regent), and the absorbance values for treated cells were recorded as a percentage of the average vehicle control absorbance value (set at 1). C


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Figure 2. BPI chromatograms in negative ion mode (A) and PCA score plot (B) of five parts of Acer truncatum: leaf (AL), branch (ABr), bark (ABa), fruit (AF), root (AR), and quality control (QC). Fruit extracts did not sufficiently dissolve in 80% ethanol; thus the fruit extracts were excluded from testing. The basic data calculations were conducted in Microsoft Excel, and IC50 values were obtained by nonlinear regression (curve fit) analysis in GraphPad software.

includes information on neutral mass, formula, CAS number, scientific name, and structure. This library promotes the metabolite characterization of A. truncatum and facilitates the identification of metabolites from other Acer species. A similar strategy can be used to create customized databases for other target genera, but the size of these chemotaxonomic databases could vary significantly depending on the number of species involved. Metabolite Characterization by Progenesis QI. The 110 LC-MS runs were imported to Progenesis QI for data processing and characterization. The peak picking resulted in the detection of 2850 metabolic features and filtered by means of ANOVA p value ≤0.05 and max fold change ≥2 to focus on the identification of significantly changing potential marker compounds. GCG was identified at 1.64 min using three adducts M − H, M + Na−2H, and 2M − H, along with the MS and retention time (Figure 1A). The experimental fragment ions of GCG match the theoretical fragments from Progenesis QI (Figure 1B,C). To further confirm the identification of

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RESULTS AND DISCUSSION In-House Library of the Genus Acer. A total of 452 compounds were identified from the related literature on phytochemistry and pharmacology of Acer (last updated December 2017), including flavonoids, phenylpropanoids, tannins, terpenoids, and other compound classes. In 2016, a review paper reported that 331 compounds had been identified from 34 species of the genus Acer as of October 2015.26 The Acer compound library includes an additional 121 compounds and one additional species, Acer chiangdaoense, which had undergone phytochemical analysis.27 Of the 452 compounds from the literature, 385 were confirmed and their .mol files were saved. The specific in-house library consists of 385 compounds previously identified from the genus Acer, and each compound D


Article

Journal of Agricultural and Food Chemistry GCG, the MS and high energy MSe data were examined. The major matched fragment ions include 475.0765 [M − H] (C22H17O11, −1.3 ppm), 915.1620 [2M − H] (C44H35O22, 1.4 ppm), 305.0664 [M − H-galloyl] (C15H13O7, 1.0 ppm), 287.0562 [M − H−gallic acid] (C15H11O6, 2.1 ppm), 243.0287 [M − H-gallic acid−CO2] (C14H11O4, −2.5 ppm), 169.0132 [gallic acid−H] (C7H5O5, −3.0 ppm), and 125.0229 [gallic acid−H-CO2] (C6H5O3, −4.8 ppm) (Figure 1C). These fragments confirmed the identification of GCG and corresponded with Progenesis QI identification. In the current study, a total of 98 compounds were identified from the different parts of A. truncatum. These compounds were classified based on their structure: 18 flavonoids; 5 terpenoids; 8 lignans; 4 coumarins; 13 phenylpropanoids; 13 phenolic acids and derivatives; 7 saccharides and derivatives; 4 lipids; 1 quinone and derivatives; 1 alkaloid; 1 polyol; and 4 others. The structural classes of the remaining 19 compounds have yet to be determined, and the structures of 19 pairs of isomers need further study to confirm their structures. Detailed information on the identified compounds, including their major ions, adducts, molecular formula, confidence score, fragmentation score, mass error, and isotope similarity, is shown in Table S1. Twenty-one identifications (marked in red in Table S1) were previously isolated or detected from A. truncatum, while another 63 compounds were reported from this species for the first time. Among them, 54 of 63 compounds have been reported from other Acer species, and the remaining 9 compounds that have not been previously identified from Acer are all primary metabolites associated with the citric acid cycle or membranes. Flavonoids are the main constituents in this species,4 especially galloylated flavonoids, such as quercetin 3-O-galloyl-rhamnoside, quercetin-3-Ogalloylgalactopyranoside, kaempferol 3-O-galloyl-rhamnoside, epicatechin 3-O-gallate, and others. Most of galloylated flavonoids displayed numerous bioactivities including antioxidant, anti-inflammatory, inhibition of α-glucosidase, and anti-HIV.28−31 A number of precursor organic acids, including shikimic acid, ursolic acid, cinnamic acid, caffeoylquinic acid, oleanic acid, and malic acid, were also identified. Biomarker Probe for Differentiating Five Parts of A. truncatum. PCA is an unsupervised method that reduces high-dimensional data into fewer dimensions in order to visualize trends and patterns.32 It has been applied to metabolomics research widely to get an overview of the differences between numerous samples. OPLS-DA is effective statistic model that performs comparison of two or three different sample groups. It also can be used for a binary samples comparison to compare one sample with the rest of the samples in an experiment. OPLS-DA highlight with S-plot is a specific strategy to find the marker compounds for the differentiation study.24 To investigate the metabolic differences of the diverse parts of A. truncatum, an untargeted metabolite profiling of AL, ABr, ABa, AF, and AR extracts were carried out and analyzed by UPLC-QTOF-MS. The base peak ion (BPI) chromatograms of AL, ABr, ABa, AF, and AR show clear differences in their overall composition (Figure 2A). To obtain a full-scale overview of chemical differences among samples, all the compound features detected in negative, without any filtering, were used for PCA. As the PCA scores plot (Figure 2B) shows, the QC samples are clustered closely together and are close to the center of the plot. The clustering of the QC samples at the center of the PCA plot indicates good reproducibility and confirms that the

observed variation among different parts is metabolite-related rather than system-related (i.e., instability during data acquisition). The ABr, ABa, and AR are clustered closely (Group 3), indicating that these three parts have similar chemical profiles (especially ABa and AR). AL (Group 1) and AF (Group 2) are far apart in the PCA scores plot, suggesting that metabolic profiles of leaf and fruit differ significantly from each other. Additionally, Group 1, Group 2, and Group 3 are clearly separated from each other, showing that the chemical compositions of leaf and fruit differ distinctly from branch, bark, and root. On the basis of the PCA, the metabolite profiles of various parts of A. truncatum are different. Branch, bark, and root share similar chemical constituents, which differ from leaf and fruit. These differences in metabolite profiles may also help to explain how these different plant parts are used. To further identify marker compounds responsible for the differentiation among Groups 1−3, OPLS-DA with an S-plot was used to compare these three groups. The detailed comparison of models and parameters are shown in Table 1. Each model has high R2 and Q2 values indicating that the models are reliable. Table 1. Values of the Statistic Parameters Obtained for Different OPLS-DA Models and Numbers of Selected Markers from S-Plot Based on UPLC-QTOF-MS Data (in Negative Mode)a OPLS-DA

S-plot

model classes

scaling

comp

R2 (cum) (%)

Group2 (−1) vs Group1 + 3 (1) Group1 (−1) vs Group2 + 3 (1) Group2 (−1) vs Group1 (1) Group3 (−1) vs Group1 + 2 (1) Group1 (−1) vs Group3 (1) Group2 (−1) vs Group3 (1) Group1 (−1) vs Branch (1) Group2 (−1) vs Branch (1)

Pareto

3

98

97

20

9

Pareto

3

98

97

19

14

Pareto

3

99

99

31

25

Pareto

3

98

98

8

11

Pareto

2

99

98

19

12

Pareto

2

97

97

16

8

Pareto

2

99

99

16

12

Pareto

2

99

99

14

21

Q2 (cum) (%)

markers in −1

markers in 1

a

Notes: Group 1 (leaf), Group 2 (fruit), Group 3 (BrBaR): the group including bark, branch, and root; Groups 1 + 2: the group including fruit and leaf; Comp: components; cum: cumulative; R2 (cum): the variation displayed by all components in the model; Q2 (cum): the accuracy of the predicted class membership by the model.

The S-plot was useful to visualize the correlation and covariance between metabolites and their model type. In the Splot, each point is an exact mass/retention-time pair. The variable contributions and correlation are shown on the X-axis and Y-axis, respectively. Therefore, the variables showing significant differences between groups will be plotted at the top right (1) and bottom left (−1), and the ions with no significant difference contribution will be plotted in the middle of the Splot. Eight model classes have been conducted to identify the marker compounds (Figure 3). The ions on both ends of Splot were selected as the candidate marker compounds (red box mark in Figure 3). E


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Figure 3. (A−H) S-plot score from the eight different comparison models along with selected candidate marker compounds from each model. Please refer to Table 1 for the reference of the groups.

0.0001). Tukey’s multiple comparisons test showed significant differences between bark and branch (p < 0.05), bark and root (p < 0.001), and GA and all of plant parts (p < 0.0001). Overall, all of these 80% ethanol extracts from A. truncatum displayed DPPH scavenging activity, with bark extracts exhibiting the highest activity. Cytotoxic Effects on HepG2 Cancer Cell Line. The leaf extract of A. truncatum has been reported to be cytotoxic in several cancer cell lines and to inhibit fatty acid synthase (FAS), an enzyme correlated with energy metabolism.9 Therefore, the liver cancer cell line HepG2 was selected to assess cytotoxicity of the extracts of different parts of A. truncatum, including leaves (AL), barks (ABa), branches (ABr), and roots (AR). For each plant part, one representative sample was selected to determine the IC50 value, and then the obtained IC50 concentration was used to test the cell viability of other samples. The IC50 of AL5 was determined to be 130.89 μg/mL (Figure 6A). The cytotoxicity of the other three leaf samples on HepG2 cells was then tested at the concentration of 130.89 μg/mL. The results (lower panel, Figure 6A) indicated that these three leaf samples exhibited a similar IC50 as AL5 (130.89 μg/mL). The IC50 of ABa3 was determined to be 47.41 μg/mL (Figure 6B). At this concentration, other bark samples showed similar cytotoxicity on HepG2 cells except that the ABa1 sample exhibited a relatively lower cytotoxicity (higher cell viability) (Figure 6B). For branch samples, the IC50 of ABr3 was determined to be 113.82 μg/mL (Figure 6C). The five other branch samples were tested in HepG2 cells at this concentration (113.82 μg/mL). Cell viability was

As a result, 255 candidate marker compounds were selected and tagged. Subsequently, these tagged markers were imported to Progenesis QI for identification. After filtering with ANOVA p value ≤0.05 and max fold change ≥2, and searching the three databases as mentioned above, 52 marker compounds were identified (Table 2). Thirteen of the metabolite structures could not be determined, which need further analysis through purification or finding the appropriate reference standards. One example of marker compound selection and corresponding variable characterization is shown in Figure 4. The S-plot score of Group 1 versus Group 3 is shown in Figure 4B. The selected marker ions at the top of S-plot are enlarged and shown in Figure 4B1, and the selected ions at the bottom of S-plot are displayed in Figure 4B2. The selected markers have significant variable averages in the comparative groups (Figure 4C) and show different variable trends between the different parts of A. truncatum. Antioxidant Capacity by DPPH Scavenging Analysis. DPPH scavenging capacity was measured to evaluate the antioxidant effect of five different parts of A. truncatum, and gallic acid was used as positive control for its high antioxidant activity. DPPH scavenging activity increased as the concentration increased. At concentrations above 250 μg/mL, the antioxidant activity showed little to no change (Figure 5A). Figure 5B shows that the bark extract had the strongest antioxidant capacity (IC50 105.02 ± 10.18 μg/mL), followed by fruit (IC50 121.73 ± 12.92 μg/mL), leaf (IC50 127.12 ± 21.14 μg/mL), branch (IC50 132.27 ± 19.80 μg/mL), and root (IC50 146.08 ± 11.30 μg/mL). One-way ANOVA analysis showed significant differences between these groups (p < F


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Journal of Agricultural and Food Chemistry Table 2. Fifty-Two Tentatively Identified Metabolites from Different S-Plot Scores (in Negative Mode)

a

UPLC-QTOF-MS no.

RT-EM

tentative identification

MF

adducts/fragments

ME

1 2 3b 4b 5

0.28_196.0580n 0.31_133.0140m/z 0.31_208.0217n 0.31_210.0738n 0.31_342.1162n

gluconic acid malic acid hydroxycitric acid33 sedoheptulose34 sucrose

C6H12O7 C4H6O5 C6H8O8 C7H14O7 C12H22O11

6 7 8

0.31_402.1377n 0.40_192.0268n 0.86_447.1507m/z

9

0.98_354.0970n

10 11

1.09_414.1769n 1.24_338.1022n

12

1.29_183.0297m/z

13 14 15 16 17 18 19 20 21b 22 23 24 25 26 27 28 29 30 31 32

1.33_478.1688n 1.40_426.0951n 1.40_578.1423n 1.40_600.1243n 1.52_290.0787n 1.60_484.1214n 1.60_866.2052n 1.64_386.1941n 1.64_387.2026m/z 1.64_405.1402m/z 1.64_434.1981n 1.64_491.1771m/z 1.64_576.1272n 1.86_594.1588n 1.95_377.1818m/z 1.95_464.0954n 1.98_616.1067n 2.02_416.2497n 2.07_470.0588n 2.10_433.0773m/z

33 34 35 36 37 38 39 40c

2.10_492.1993n 2.22_447.0930m/z 2.26_478.1120n 2.26_546.0654n 2.41_372.2155n 2.45_432.1059n 2.88_585.2348m/z 3.10_448.2316n

41c 42c 43c

3.41_346.2356n 3.65_330.2406n 5.63_293.2124m/z

44 45c 46c 47 48c

5.86_595.2892m/z 5.98_311.2224m/z 6.13_295.2278m/z 6.25_571.2895m/z 6.44_293.2122m/z

49 50c 51 52

6.67_566.3467m/z 6.87_295.2278m/z 7.72_277.2175m/z 7.96_833.5183m/z

unknown citrate β-D-glucopyranoside 4(hydroxymethyl)-2-methoxyphenyl 6-O-D-apio-β-D-furanosyl 5-O-caffeolyquinic acid or chlorogenic acid unknown 4-coumaroylquinic acid or cinnamic acid 3 (or 4)-O-methylgallic acid or methyl gallate kelampayoside A catechin-3-(3,4-dihydroxy)-benzoate procyanidin B2/B3 unknown catechin/epicatechin unknown procyanidin C1/C2 citroside A litchioside C35 junipediol A 8-glucoside or nikoenoside unknown unknown proanthocyanidin A6 kaempferol 3-neohesperidoside unknown myrictitrin hyperin 6′′-gallate unknown unknown morin-3-O-lyxoside or quercetin 3-O-βarabinofuranoside or quercetin 3-Oarabinopyranoside or guaijaverin or avicularin schizandriside quercitroside isorhamnetin 3-glucoside unknown icariside B6 afzelin acernikol orisomer geranyl-6-O-β-D-xylopyranosyl-β-Dglucopyranoside36 dihydroxy-octadecanedioic acid37 trihydroxy-octadecaenoic acid isomer37 hydroxy-octadecadienoic acid isomer/ oxo-octadecadienoic acid isomer37 unknown dihydroxy-octadecadienoic acid38 9-hydroxy-octadecadienoic acid37 unknown hydroxy-octadecadienoic acid isomer/ oxo-octadecadienoic acid isomer37 unknown hydroxy-octadecadienoic acid isomer37 γ-linolenic acid glycerophospholipids

−1.68 −2.18 −1.23 −0.86 0.05

C14H26O13 C6H8O7 C19H28O12

M-H, M+Na-2H, 2M-H M-H, M-H2O-H M-H2O-H, M-H, 2M-H M-H2O-H, M-H M-H, 2M-H, 2M+FA-H, M+Na-2H, M+FA-H, M-H2O-H M+Na-2H, M+K-2H M-H, M+Na-2H M-H, M-H2O-H

C16H18O9

M-H, 2M-H, M+Na-2H

5.35

√

C17H26N4O8 C16H18O8

M-H, M+K-2H M-H, M+Na-2H

4.46 6.1

√

C8H8O5

M-H

−1.14

√

C20H30O13 C22H18O9 C30H26O12 C27H24N2O14 C15H14O6 C21H24O13 C45H38O18 C19H30O8 C19H34O9 C16H24O9 C16H34O13 C21H26N4O7 C30H24O12 C27H30O15 C16H28O7 C21H20O12 C28H24O16 C15H36N4O9 C21H14N2O11 C20H18O11

M-H, 2M-H, M+Na-2H M-H2O-H, M-H M-H, 2M-H M-H, M+FA-H, M+K-2H M-H, 2M-H, M+Na-2H M-H2O-H, M+FA-H, M-H M-H, M+K-2H, M+Na-2H M-H, M+FA-H M-H2O-H M+FA-H M-H2O-H, M-H M+FA-H M-H, 2M-H M-H, 2M-H M+FA-H M-H, 2M-H M-H, M+Na-2H M+K-2H, M+FA-H, M-H M-H, M+K-2H, 2M-H, M+Na-2H M-H, M-H2O-H

0.3 −0.01 −0.18 2.64 −1.32 −0.62 3.97 −0.03 0.39 −0.22 −4.3 −2.84 0.69 0.61 0.35 −0.18 0.36 3.47 −1.96 −0.74

C25H32O10 C21H20O11 C22H22O12 C25H14N4O11 C19H32O7 C21H20O10 C31H38O11 C21H36O10

M-H, M-H, M-H, M-H, M-H, M-H, M-H M-H,

−0.51 −0.72 1.88 −0.92 1.86 0.5 1.1 1.7

C18H34O6 C18H34O5 C18H30O3

M-H2O-H, M-H M-H, M+Na-2H M-H

0.3 −0.19 0.72

C21H18N2 C18H32O4 C18H32O3 C27H50O10 C18H30O3

2M-H M-H M-H M+K-2H M-H

4.22 −1.33 −0.39 0.86 −0.01

C28H47N3O6 C18H32O3 C18H30O2 C43H79O13P

M+FA-H M-H M-H M-H

3.86 −0.16 0.57 −0.27

G

M+Na-2H, M+FA-H M-H2O-H, 2M-H 2M-H 2M-H, M+Na-2H M+FA-H 2M-H M+FA-H

AL

ABr

ABa

AR

AF

√

√ √ √ √ √

√ √

√ √

0.85 −1.16 −0.14

√

√

√

√ √

√ √ √ √ √

√

√

√ √ √ √ √ √

√ √ √ √ √ √

√

√ √ √ √

√

√ √ √ √ √

√ √

√ √ √ √ √ √

√

√

√

√

√

√ √ √ √

√

√

√ √ √ √ √ √ √


Article

Journal of Agricultural and Food Chemistry Table 2. continued

a MF: molecular formula; ME: mass error (ppm). “√” mean detected from organs, “blank” means very low intensity or undetected. bIdentified by searching the structure of formula in SciFinder and then refining the references which included the structures by Acer or Sapindaceae. cIdentified by checking the MS and MSe data of samples by manual and comparing the MS data with published values.

Figure 4. (A−C) An example of marker compound selection and variable trends in Acer.

Figure 5. Comparative DPPH scavenging capacity of A. truncatum. (A) DPPH scavenging activity of different plant parts; (B) IC50 values of different plant parts and GA (13.32 ± 0.24 μg/mL). The results were expressed as mean ± standard deviation, with triplicate experiments for each sample.

Potential Biomarkers from the Bark Extracts. Most investigations of A. truncatum antioxidant and cytotoxity activities have focused on AL extracts. In this study, other parts of A. truncatum were also shown to exhibit significant antioxidant and cytotoxic activity. Furthermore, ABa extracts displayed higher bioactivity than AL extracts. The PCA scores plot (Figure 2) indicates that the chemical composition of ABa and AL are clearly different. OPLS-DA along with S-plot was used to identify potential biomarkers corresponding to the differences observed in the PCA scores plot (Figure 7). The group differences between ABa and AL are shown in the OPLS-DA statistical model with the cumulative R2Y value of 0.99 and cumulative Q2 value of 0.99, which cross-validated the excellent fit of the model (Figure 7A). On the basis of the

around 50% (for ABr1 and ABr6) or slightly lower (for ABr2, ABr3, ABr4, and ABr5) (Figure 6C), suggesting that IC50 for each of these five branch samples is similar to ABr3 (113.82 μg/mL). The IC50 of root sample AR5 was determined as 49.57 μg/mL (upper panel, Figure 6D). When cells were treated with each of the different root extracts at this concentration, cell viability was consistently around 50% for all samples (Figure 6D), suggesting that root samples have IC50 of similar to AR5 (49.57 μg/mL). Taken together, all of the four parts (AL, ABa, ABr, and AR) of A. truncatum display certain levels of cytotoxicity. However, the ABa extracts displayed the highest activity, followed by AR, ABr, and AL extracts. H


Article


Figure 6. Cytotoxicity on HepG2 cancer cells of the extracts from different parts of A. truncatum. (A) IC50 of sample leaf 5 (AL5, upper panel) and cell viability under the treatment with different leaf samples (lower panel). (B) IC50 of sample bark 3 (ABa3, upper panel) and cell viability under the treatment with different bark samples (lower panel). (C) IC50 of sample branch 3 (ABr3, upper panel) and cell viability under the treatment with different branch samples (lower panel). (D) IC50 of sample root 5 (AR5, upper panel) and cell viability under the treatment with different root samples (lower panel). V: vehicle control.

Figure 7. Selected Acer truncatum potential biomarkers from the comparative analysis of bark and leaf based on S-plot. (A) OPLS-DA analysis; (B) S-plot show the selected markers; (C) VIP score of selected markers; (D) variable averages by group of selected potential marker compounds.

S-plot analysis, 12 potential marker compounds were selected to chemically distinguish the ABa from the AL (Figure 7B). The VIP plot (Figure 7C) shows that all of the 12 selected potential marker compounds in ABa have a high VIP value (VIP > 3), indicating these marker compounds are largely responsible for the discrimination between ABa and AL, chemically. Furthermore, the variable average by group clearly shows the different levels of the selected marker compounds in ABa and AL (Figure 7D). The retention time-extracted mass, MS data, corresponding high energy fragments, mass error

values, VIP values, and identifications of the 12 selected markers are shown in Table 3. As shown in Figure 7B−D and Table 3, the top three potential marker compounds differentiating ABa from AL with m/z 289.0618 (RT 1.52 min), 577.00771(RT 1.42 min), and 864.9702 (RT 1.60 min). The corresponding compounds could play key roles in the higher bioactivity of ABa relative to AL. The ion 289.0618 (RT 1.52 min) was tentatively identified as a derivative of (+)-catechin. This identification was made based on the molecular ions at m/z 289.0712 [M − H]− and m/z 291.0866 [M + H]+ and I


Article


Table 3. Tentative Identification of Potential Biomarkers Selected from Acer truncatum Bark Based on the S-Plot of Bark Versus Leaf .MS molecular ions [M− H]−/ [M + H/Na]+ m/z

MSe fragment ions at negative mode

no.

RT-EM

MF

ppm

m/z

MF

ppm

VIP

1

0.79_432.6407

433.1344 457.1316

C18H25O12 C18H26O12Na

−0.5 −1.3

447.1507

C19H27O12

0.9

−0.8 1.7 3.0 3.9

unknown

0.90_447.0880

C15H21O10 C11H17O9 C9H13O7 C20H29O14

3.52

2

361.1132 293.0878 233.0668 493.1576

4.19

β-D-glucopyranoside, 4-(hydroxymethyl)-2methoxyphenyl 6-O-D-apio-β-Dfuranosyl49

471.1471

C19H28O12Na

−1.5

353.1458 293.0876 425.0886 407.0770 289.0713 125.0239 865.1995 695.1426 577.1317 245.0816 203.0708 713.1497 695.1401 577.1353 425.0866 407.0762 289.0714 245.0451 125.0239

C14H25O10 C11H17O9 C22H17O9 C6H5O3 C22H15O8 C15H13O6 C45H37O18 C37H27O14 C30H25O12 C14H13O4 C12H11O3 C37H29O15 C37H27O14 C30H25O12 C22H17O9 C22H15O8 C15H13O6 C13H9O5 C6H5O3

2.8 1.0 3.1 0.7 0.3 0 1.7 3.6 −5.0 0.8 0 −1.3 0.4 1.2 −1.6 −1.2 0.7 0.4 0

521.2023 449.0865 407.0761 289.0710 267.1957 223.2060

C26H33O11 C24H17O9 C22H15O8 C15H13O6 C16H27O3 C15H27O1

0.6 −1.8 −1.5 −0.7 −1.1 −0.9

293.2120 249.2218 279.2328 277.2182 255.2304

C18H29O3 C17H29O1 C18H31O2 C18H29O2 C16H31O2

1.0 0 1.4 5.1 −7.8

3

1.42_577.0077

577.1343 579.1498

C30H25O12 C30H27O12

−0.5 −0.9

4

1.44_1155.3420

1153.2616 1155.2743

C60H49O24 C60H51O24

0.2 −2.3

5

1.52_289.0618

6

1.60_864.9702

289.0712 291.0866 865.1965 867.2127

C15H13O6 C15H15O6 C45H37O18 C45H39O18

0 −1.0 −1.7 −1.0

7 8

1.64_719.8936 1.84_581.0145

unidentified 581.2230

C28H37O13

−0.7

9

5.49_285.3076

285.2065

C16H29O4 C16H31O4

−0.4 −1.7

10 11

5.55_265.1325 5.99_311.1926

12

6.18_295.6224

unidentified 311.2222 335.2188 295.2272 319.2237

C18H31O4 C18H32O4Na C18H31O3 C18H32O3Na

0 −3.0 −0.3 −3.8

high energy fragment ions at m/z 245.0816 due to loss of CO2 (44 Da), which has been previously reported in A. truncatum2 and confirmed by coinjection with its reference standard. The ion 577.0077 (RT 1.42 min) is likely derived from procyanidin B2 or its isomer procyanidin B3 based on the parent ions at m/ z 577.1314 [M − H]− and m/z 579.1498 [M + H]+. The main fragment ions produced includes 425.0886 due to loss of galloyl moiety (152 Da), at m/z 407.0770 due to further loss of H2O, and at m/z 289.0713 due to the loss of catechin. The parent ions and the main fragmentation profile matched the published MS data for procyanidin B2 or B3.39,40 The ion 864.9702 (RT 1.60 min) was tentatively identified as procyanidin C1 or C2 with a [M + H]+ ion at m/z 867.2127 and [M − H]− ion at m/z 865.1965, with calculated molecular formulas of C45H39O18 (−1.0 ppm) and C45H37O18 (−1.7 ppm), respectively. The main fragment ion at m/z 577.1353 with a loss of one catechin monomer (289 Da) and the fragment at m/z 289.0714 (catechin) due to the loss of another catechin, showed the main fragmentation patterns of

11.47

4.51

12.86

identification (ref)

procyanidin B2 or procyanidin B339,40

cinnamtannin A250

(+)-catechin2, coinjection

8.43

procyanidin C1 or procyanidin C241,42

4.04 3.60

unknown

3.15

hexadecanedioic acid51

3.31 6.66

dihydroxyoctadecadienoic acid37

3.42

13 (or 9)-hydroxylinoleic acid52

procyanidin C1 or C2 and corresponds with reported data from the literature.41,42 The structures of these compounds are shown in Figure 8. Catechins are well-known bioactive compounds in many edible plants, including green tea (Camellia sinensis). Catechins have potent antioxidant capacity for metal ion-chelation and against reactive oxygen species, and much research on the antioxidant properties of catechins have been reviewed in detail.43 In addition, it is also reported that catechins can inhibit tumor growth, tumor angiogenesis, and cancer cell invasion.44 Procyanidins have been investigated for their wide variety of bioactivities and have been shown to have cytotoxic, antioxidant, antiangiogenic, and α-glucosidase inhibition activity, and the ability to induce apoptosis.45−47 For example, procyanidin B3 from peanut skin has exhibited antiproliferative effects in DU145 cells (human prostate carcinoma cell) by increasing intracellular ROS level, decreasing Bcl-2/Bax ratio, and triggering p53 and caspases-3 in DU145 cells. 39 Procyanidin B3 has also shown high inhibitory activity on αJ


Article


Figure 8. Chemical structures of significant potential marker compounds from the bark of Acer truncatum.

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glucosidase.40 Gao et al. successfully integrated UPLC-QTOFMS with a modified ABTS antioxidant analysis system and a fluorescent probe to screen and prove the significant antioxidant activity of procyanidin B2 and procyanidin C2.41 Zhao et al.9 have reported gallated catechins, such as EGCG, ECG, and GCG, showed inhibitory effects on FAS. The cytotoxic markers we identified, (+)-catechin and procyanidins, are both also catechin derivatives, and these compounds may also target FAS, but more experiments are needed in the future to look into possible structure−activity relationships. Other researchers, who have studied the antitumor and FAS inhibitory activity of AL extracts, found that galloylated compounds, mainly galloylated flavonoids and gallotannin like pentogalloglucose (PGG), are important bioactive consituents.5,9,11,48 However, PGG was not detected from ABa as a significant marker compound in this study, likely because it is distributed in both AL and ABr, and not significantly different in content. In the current study, (+)-catechin, procyanidin B2/B3, and procyanidin C1/C2 as the specific marker compounds from ABa could support the chemical basis for the higher bioactivity of ABa extracts relative to AL extracts, and provide a new direction to study the constituents with antitumor and FAS inhibitory activity from A. truncatum. Our ongoing isolation of A. trancatum marker compounds will further confirm the tentative identification by MS.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.8b04035.

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All compounds identified from Acer truncatum based on Progenesis QI software (Table S1) (PDF)

AUTHOR INFORMATION

Corresponding Authors

*(C.L.L.) E-mail: [email protected], [email protected]. cn. Tel: +86 10 68930381. *(E.J.K.) E-mail: [email protected]. Tel: +1 718 9601105. ORCID

Edward J. Kennelly: 0000-0002-1682-2696 Funding

We are grateful for financial support from the Ministry of Education of China and State Administration of Foreign Experts Affairs of China (B08044), National Natural Science Foundation of China (31761143001 and 31870316), Key Laboratory of Ethnomedicine (Minzu University of China) of Ministry of Education of China (KLEM-ZZ201806), Minzu University of China (Collaborative Innovation Center for Ethnic Minority Development and YLDXXK201819), the China Scholarship Council (CSC No. 201606390031), and the Doctoral Research Project of Minzu University of China (2016). L.R. was supported in part by the Macaulay Honors College Opportunities Fund. K


Article

Journal of Agricultural and Food Chemistry Notes

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The authors declare no competing financial interest.

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ABBREVIATIONS UPLC-QTOF-MS, ultraperformance liquid chromatography coupled quadrupole/time-of-flight mass spectrometry; LC-MS, liquid chromatography tandem mass spectrometry; HRMS, high-resolution mass spectrometry; DIA, data-independent acquisition; MSe, mass spectrometryelevated energy; OPLS-DA, orthogonal partial least-squares discriminant analyses; PCA, principal component analysis; VIP, variable influence on projection;; CCS, collisional cross section; AL, leaf of Acer truncatum; ABr, branch of A. truncatum; ABa, bark of A. truncatum; AF, fruit of A. truncatum; AR, root of A. truncatum; PGG, 1,2,3,4,6-penta-O-galloyl-β-D-glucose; GCG, (−)-gallocatechin-3-O-gallate; ECG, (−)-epicatechin-3-O-gallate; EGC, (−)-epigallocatechin; EGCG, (−)-epigallocatechin-3-O-gallate; GA, gallic acid; DPPH, 2,2-diphenyl-1-picrylhydrazyl; DMSO, dimethyl sulfoxide; FAS, fatty acid synthase; MCF-7, human breast cancer cell lines; CAES-17, human esophagus cancer cells; BEL-7402, human hepatocellular carcinoma cells; BGC-823, gastric carcinoma cells; CNKI, China Knowledge Resource Integrated database

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